Mark Hendy

Category: CFO Insights

  • The Accounts Are Clean. Companies House Still Thinks You Owe the 2006 Loan.

    The Accounts Are Clean. Companies House Still Thinks You Owe the 2006 Loan.

    Most PE diligence still starts in the same place: last signed accounts, latest management pack, a debt schedule that ties. If the balance sheet shows no bank loan, the room relaxes. Then somebody opens Companies House and finds a charge from 2006 sitting there like a bad smell that never got a window opened.

    The accounts can be right. The public record can still be wrong. A buyer, a bank, or a new director will treat the register as the truth. That is the point of a public register.

    The gap nobody puts in the data room index

    A charge on the register is not the same thing as a loan on the balance sheet. One is a legal security interest recorded at Companies House. The other is an accounting residual. They are supposed to move together. In owner-managed groups, hall companies, old family holdcos and plenty of otherwise tidy PE portcos, they do not.

    The usual story is boring, which is why it survives. The facility was repaid. The refinance completed. The overdraft died with the old bank. Nobody filed the satisfaction. Ten years later the directors have changed, the auditors have changed, and the only person who remembers the original completion file is retired. The Companies Act 2006 Part 25 charge regime does not auto-clean itself because your cash account looks healthy.

    Why a dead loan still looks alive

    Since April 2013, most UK company charges go on with form MR01. Getting them off is a separate act: a statement of satisfaction, MR04, in full or in part. If the company no longer owns the charged property, that is a different filing. None of this happens because the loan note hit zero in the TB.

    Older all-monies bank charges are worse. They were often taken as a standing security over “everything we might ever owe you”, then left in place through three refinances and a change of clearing bank. The debt is gone. The public footprint is not. Credit reference agencies and some KYC shops still read the register, not your verbal history of the relationship.

    There is no useful statute of limitations that makes an unsatisfied charge evaporate. It sits. It ages. It looks like a problem to anyone who was not in the room when the cheque cleared.

    What a buyer, a bank, or a new director actually sees

    A new director doing even a light personal check will see outstanding charges and no matching liability. That is not a trivia question. It is the first test of whether finance knows the difference between the books and the public record. If you are asking someone to take a board seat, do not make them discover this on a Sunday night.

    A buyer’s counsel will not accept “everyone knows that one is historic.” They will want the lender’s confirmation and the satisfaction filed, or a clean explanation that survives a completion checklist. A debt fund doing holdco diligence will ask the same question in a worse tone.

    This is also why “the accounts are clean” is not a diligence conclusion. It is a starting position. I have written before about what the interim CFO job actually is. It is not to decorate a data room. It is to make the public record, the bank, and the pack tell the same story before somebody else notices they do not.

    The twenty-minute test

    Before you take a board seat, buy a book, or sign a completion agenda, do this:

    1. Pull the company on Find and update company information. Open charges. Note created date, chargees, and whether satisfaction has been filed.

    2. Put that list next to the last filed accounts — creditors notes, contingent liabilities, security disclosures — and the current debt schedule.

    3. Anything on the register with no loan line is not a mystery. It is an open item. Either the books are missing a liability, or the register is missing a satisfaction. Both are finance problems. Only one of them is usually true. You still have to prove which.

    4. If the chargee still exists, ask for written confirmation the facility is gone. Then file. If the chargee has been through three mergers and a name change, that is a research job, not a reason to leave it.

    Twenty minutes. Sometimes twenty days if the old bank has to find a deed. Either way, do not discover it in week six of a 100-day plan.

    File the satisfaction. Then stop calling it historic.

    Companies House is not being difficult. It will record what you file. The event-driven filing rules exist because the register is used by people who do not have your shared drive. An unpaid historic charge is not a vibe. It is an unfiled event.

    If you are the CFO, this is a control, not a tidy-up. Put “CH charges vs debt schedule” in the monthly close pack until the list is nil or explained. If you are the incoming interim, do it in week one, before you start talking about systems, AI, or the covenant case. A model that cannot see an unsatisfied charge is not intelligence. It is a very fast way to reprint the same gap.

    I am not giving legal advice. Get counsel on anything with a live lender, a disputed repayment, or property still sitting in the security pool. The operational point stands without a QC: the public record will be treated as true until you change it.

    The PE tell

    Houses that actually underwrite operations will ask for the charges print on day one. Houses that buy a narrative will notice it when the lawyers do, which is later and more expensive. If your AI stack, your QofE, and your board pack all missed a 2006 charge that outlived the loan, the problem was not the charge. The problem was the definition of done.

    Clean accounts are necessary. They are not sufficient. File the satisfaction. Then the story you are telling investors is the same story Companies House is telling strangers.

  • If Your AI Doesn’t Change Monday Morning, It’s Theatre

    If Your AI Doesn’t Change Monday Morning, It’s Theatre

    Most PE boards now have an AI slide somewhere in the pack.

    It usually looks impressive. A chatbot for policies. A prettier forecast chart. A memo that took forty minutes instead of four hours. Nobody wants to admit the awkward part: none of that changed Monday morning.

    If you are the interim CFO walking into a PE-backed holdco, that distinction is the whole job. There is AI that compresses the close, sharpens covenants and tells the truth about cash. And there is AI that writes nicer decks. Only one of them moves a hold period.

    Theatre is not neutral

    Theatre is expensive because it steals attention. Sponsors hear “AI in finance” and assume the control environment just got tighter. What they often got is a thin wrapper over the same late pack, the same reconciling nightmare, and the same working-capital surprise three days before the lender call.

    I am not anti-tool. I run a serious AI stack in my own work. The test is brutal and fair:

    Did a decision move earlier, with better evidence, than it would have last quarter?

    If the answer is no, you bought content production. Call it that. Do not call it transformation.

    Three workflows that actually move a PE hold

    Ignore the vendor map for a minute. In a typical mid-market PE asset, three finance workflows earn their keep.

    1. Close compression that is real, not cosmetic.
    The close is still where trust is made or destroyed. AI helps when it attacks the bottleneck chain: flux narratives drafted from the actual trial balance movements, exception queues ranked by materiality, intercompany breaks clustered by pattern instead of hunted one by one. It does not help when someone pastes a half-reconciled P&L into a chatbot and asks it to “explain variance” with no tie-back to source.

    What good looks like: board pack numbers lock earlier, commentary cites the same source the controller trusts, and the CFO stops spending Sunday night rewriting slides that should have been true on Thursday.

    2. Covenant and cash early-warning — before the breach conversation.
    Lenders do not care that your deck is eloquent. They care whether you saw the turn in advance. The useful stack watches bank actuals, order book, receivables ageing and inventory truth on a cadence shorter than the monthly myth. Models can flag trajectory toward a covenant headroom problem while you still have levers. Models cannot invent headroom you already spent.

    If your “AI treasury insight” cannot show the last thirteen weeks of cash and the next eight with honest driver notes, it is jewellery.

    3. Working-capital truth that survives diligence tone.
    PE holds live and die on cash conversion. AI is useful when it forces the ugly questions into the open: who is shipping without billing discipline, which SKUs are museums, where overdue is a commercial choice dressed up as admin lag. The output should change collections behaviour and purchasing behaviour — not produce a heat map nobody acts on.

    Exit narratives love “AI-enabled operations.” Buyers’ diligence teams love bank statements. Align yourself with the second group.

    What to starve

    Be rude about the rest, at least internally.

    • Generic chat over unstructured drives with no retention, no permissions model, and no citation path back to the ERP.
    • Auto-generated board prose that smooths over a late close. Pretty wrong is still wrong.
    • Pilot theatre that never touches the month-end calendar, the bank feed, or the covenant workbook.
    • Tool sprawl where every function buys a different assistant and finance inherits the reconciliation of the assistants.

    If a tool cannot name the system of record it reads, the control owner, and the decision it accelerates, it is a toy with an invoice.

    The interim CFO test in the first thirty days

    When I land in a PE-backed finance function, I do not start with a vendor bake-off. I start with the Monday morning stack:

    • What does the CEO actually ask every week?
    • What does the board pack still get wrong under pressure?
    • Where does cash surprise still live?
    • Which close tasks burn senior time that a junior plus a model should own?

    Then we wire AI into those seams — with human sign-off still sitting on anything that hits lenders, auditors or public numbers. AI amplifies the operating system you already have. If the operating system is chaotic, AI makes the chaos faster. That is not a technology failure. That is a leadership tell.

    For sponsors and chairs

    Ask better questions in the next IC or board slot:

    • Which decision moved forward by at least one week because of this tool?
    • What is the system of record, and who signs the output?
    • Did close day-count, covenant headroom visibility, or cash conversion change in a measurable way?
    • What did we stop doing because the model took the grind?

    If the answers are all narrative, you are funding theatre. Theatre photographs well in a value-creation plan. It does not reprice an exit.

    The point

    The PE cycle is rewarding operators who can see clearly under stress. AI belongs in that story only when it shortens the distance between messy reality and a decision a grown-up will own.

    Nicer decks are optional. Monday morning is not.

  • Only 36% of Interim CFOs Go Permanent in PE. Stop Pretending the Job Is an Audition.

    Only 36% of Interim CFOs Go Permanent in PE. Stop Pretending the Job Is an Audition.

    Most people treat the interim CFO seat at a PE-backed business as a waiting room for the permanent job.

    The data says that is a fantasy.

    Research covered by CFO.com on Barton Partnership work puts the conversion rate from interim to permanent at PE-backed firms at roughly 36%. Only about four in ten interim finance chiefs even said they were open to staying under the right conditions. The rest are doing something else entirely: closing a capability gap, stabilising a reporting stack, carrying a business through diligence or exit, then moving on.

    If you sit on an investment committee, that number should change how you hire. If you are the interim, it should change how you negotiate.

    The job is not a try-before-you-buy

    Sponsors still talk about interim CFOs as if the assignment is a six-month audition. Sometimes it is. More often it is a deliberate instrument:

    • the deal thesis needs a finance leader who has already lived a similar hold period;
    • the sitting FD cannot carry lender packs, board cadence and systems cleanup at once;
    • management wants a grown-up in the room without locking a permanent package before the first 100 days are honest;
    • exit is visible and nobody wants a brand-new permanent CFO learning the business in the CIM.

    That is not a failed permanent search. That is a different product.

    When boards blur the two, they get the worst of both: an interim who half-applies for the permanent seat, and a permanent hire who was never properly scoped.

    Why conversion is low — and why that is often healthy

    Low conversion is not automatically a governance failure. In PE it is frequently the design.

    1. The skill that saves the hold is not always the skill that runs the next five years.
    Rescue, refinance, ERP recovery, TP cleanup, warehouse go-live, carve-out — these are campaign sports. Steady-state FP&A leadership, culture and long-cycle talent development are different muscles. Pretending one person must be both is how you overpay for the wrong profile.

    2. The best interims are expensive because they are liquid.
    People who can land cold into a PE board pack and make it legible do not need your permanent role to feel successful. They need a clean mandate, decision rights, and a finish line. If your only retention tool is “maybe we will make it permanent,” you are bidding with monopoly money.

    3. Sponsors already know who they might want long-term.
    Often the permanent CFO is a known quantity from a prior portco, a portfolio talent map, or a search that was always going to conclude after the fire was out. The interim was never in that race. Telling them otherwise is theatre.

    4. Conversion politics punish honesty.
    If the interim is quietly campaigning for the seat, bad news arrives late. If the board pretends the door is open when it is not, trust collapses in month four. Clear “this is a closed-ended assignment” language is kinder and more commercial than soft ambiguity.

    What good PE sponsors do instead

    The high-functioning pattern is boring, which is why it works.

    Name the product on day one. Stabilise / professionalise / exit-ready / systems rebuild / fundraise support. One primary job. Secondary jobs in writing, not in hallway vibes.

    Separate the permanent search clock from the interim clock. If you might convert, say what evidence would justify it and when that decision will be taken. If you will not convert, say that before the first board. Ambiguity is not optionality. It is unmanaged risk.

    Pay for outcomes, not for hope. Day rate or project fee against deliverables beats a discounted permanent package with a whispered upside. Interims who accept underpriced “try-outs” train sponsors to treat senior finance as a temp bench with equity cosplay.

    Instrument the handoff. The value of a strong interim is not only the three months of packs. It is the operating system left behind: close calendar, board pack skeleton, cash bridge discipline, covenant early-warning, decision log, open diligence Q&A. If that does not exist at exit from the assignment, you rented a person. You did not buy capability.

    What the interim should demand

    From the other side of the table — and I sit there often enough — the commercial hygiene is simple.

    Mandate in writing. What “done” looks like. What is out of scope. Who can overrule you. Which systems you own versus babysit.

    Decision rights on cash and reporting. An interim CFO without authority over the cash bridge and the board pack is a commentator with a nicer title.

    A clean conversion clause — or none. Either a dated decision gate with criteria, or an explicit non-conversion statement. Soft “we will see how it goes” is how both sides waste political capital.

    Permission to build past yourself. If the assignment succeeds, the business should need you less at the end than at the start. That is the point. Hire the number two, fix the calendar, kill the heroics. Sponsors who punish that behaviour are selecting for dependency.

    And get the tax wrapper right. A real PE interim is almost always outside IR35 when structured properly: own company, own tools, substitution/control reality, financial risk, and a finished assignment rather than a disguised employment. If the commercial deal is temporary employee with a day rate, you have already lost the product definition and invited a status fight you do not need. Sponsors who want interim outcomes should buy a genuine B2B assignment. Interims who want the economics of independence should not pretend they are on a probationary payroll.

    Where AI changes the interim brief

    This is no longer only a people story.

    A modern interim CFO is often dropped into a business that still closes in Excel folklore while the sponsor deck claims “AI-enabled value creation.” The gap is becoming the job.

    • Can the finance stack produce lender-grade cash visibility without a weekend of heroics?
    • Are AI tools allowed to touch the close, the pack, the covenant model — and under whose control?
    • Is “productivity” just headcount hope, or a measured reduction in cycle time and error rate?

    I have written separately about measuring AI and local AI. The interim angle is blunter: if you only have 90–180 days, you cannot wait for a transformation theatre programme. You need a short list of automations that harden the close, the pack and the cash story before the next IC.

    That is why conversion rates miss the point. The question is not “did we keep them?” The question is “is the business more finance-operable than when they arrived?”

    The PE take

    Interim CFO work at PE-backed companies is a professional service with a balance-sheet consequence, not a dating app for permanent hires.

    Use it that way.

    Hire for the campaign you are actually in. Pay for the outcome. Decide conversion on purpose, early, in writing. Measure success by the operating system left behind — not by whether the temp badge got upgraded.

    And if you are the interim: stop auditioning for a role nobody agreed was open. Do the job that was bought. Leave the business harder to break than you found it.

    That is the product. Everything else is soft focus.

    Mark Hendy is a PE-facing interim CFO and founder of Tanous. Views his own. Conversion statistics referenced from public secondary reporting of Barton Partnership research via CFO.com; verify primary materials before relying on the figure in a live search process.

    Sources / further reading:
    CFO.com on interim-to-permanent conversion at PE-backed firms ·
    Finatal interim finance insights ·
    CFO optimism on AI impact ·
    The CFO Who Can’t Measure AI ·
    The CFO Case for Local AI

  • The $1.65 Trillion Footnote: Big Tech’s Off-Balance-Sheet AI Debt

    The $1.65 Trillion Footnote: Big Tech’s Off-Balance-Sheet AI Debt

    A Nikkei investigation has put a number on something markets prefer to keep in the footnotes.

    According to reporting amplified this week by HedgieMarkets, Alphabet, Microsoft, Amazon, Meta and Oracle are carrying roughly $1.65 trillion in obligations that do not sit neatly on the balance sheet as debt — more than the $1.35 trillion they officially report. The pile is built from GPU contracts, data-centre leases and joint ventures that accounting rules often keep off the face of the statements until facilities go live.

    Meta alone is said to account for about $420 billion of that hidden stack — triple its reported debt in the framing of the report. Oracle’s off-balance-sheet exposure has exploded over a few years. All five companies declined to comment in the coverage.

    If those figures are even roughly right, investors reading this earnings season are not looking at the full leverage picture. They are looking at the part that fits on a summary slide.

    This is not a fraud story. It is a timing story.

    That distinction matters. Lease accounting, executory contracts, take-or-pay compute deals and project structures can be entirely legal and still economically enormous. The BIS had already waved at this as shadow borrowing. Nikkei’s contribution is less moral panic than quantification: someone added up the commitments and refused to pretend the footnotes were decoration.

    In CFO language: reported debt is not the same thing as economic leverage. One is a presentation category. The other is what still has to be paid, powered, utilised or impaired if demand disappoints.

    Why AI capex makes the old tricks dangerous again

    Data centres are not ordinary office leases. They are long-duration, power-hungry, chip-dependent industrial assets with brutal technological depreciation risk. If model demand, pricing power or utilisation come in below the pitch deck, you do not get a gentle roll-off. You get:

    • leases and service contracts hitting the accounts as facilities go live;
    • impairments on specialised shells and power arrangements;
    • stranded capacity funded by private credit, project bonds and insurance balance sheets;
    • a sudden market rediscovery that “asset-light” was a drafting choice, not a physical fact.

    The bull case says hyperscalers can absorb it because cash flow is immense and AI demand is structural. Maybe. The bear case does not require a collapse in AI — only a miss versus the capacity already contracted.

    What boards should actually ask

    If you sit on a PE board, a credit committee, or a corporate treasury that sells into this ecosystem, stop arguing about vibes and ask for a one-page economic exposure map:

    1. What is on the balance sheet, and what is only in commitments?
    Split reported debt, lease liabilities, purchase obligations, residual value guarantees, JV funding lines and take-or-pay compute. If management cannot reconcile the footnote total to a cash timeline, that is the finding.

    2. What is the go-live cliff?
    Off-balance-sheet is often just delayed on-balance-sheet. When do sites energise? When do minimum payments begin? What percentage of the $1.65T becomes unavoidable over 24 / 48 / 60 months?

    3. Who really holds the downside?
    Hyperscaler, landlord, chip vendor, private-credit lender, insurer, municipal power counterparty? AI buildout has a habit of distributing risk to people who thought they were funding “infrastructure,” not underwriting model demand.

    4. What utilisation breaks the story?
    Not the CEO’s base case. The case where GPU pricing falls, delayed model monetisation shows up, or enterprise AI seats grow slower than capacity. Sensitivity tables beat adjectives.

    5. Are covenants and ratings looking at the right denominator?
    If leverage metrics ignore the commitment stack, your “conservative” credit story is a formatting preference. Rating agencies and relationship banks are already late to some of this; do not wait for them to discover it in a downgrade note.

    Earnings season will not headline the footnote

    Four of the five names are in the near-term reporting window. The clean debt numbers will look manageable. Buybacks and capex guides will dominate the copy. The $1.65 trillion, if accurate, will remain scattered across commitments, leases and structured vehicles that do not fit a CNBC lower-third.

    That is exactly why it is interesting. Markets are very good at pricing the number on the scoreboard. They are worse at pricing the obligation that becomes a number later.

    The CFO take

    I am not arguing that Big Tech is secretly insolvent. I am arguing that AI infrastructure has reintroduced old-fashioned leverage under new labels, and that PE-facing finance teams should treat off-balance-sheet capacity commitments with the same seriousness they once reserved for opco/propco splits, take-or-pay energy contracts and vendor financing.

    Legal is not the same as small. Footnoted is not the same as optional. And “until the data centre goes live” is not the same as “risk has not yet been created.”

    If the Nikkei stack holds up under filing-level scrutiny, the next cycle’s post-mortem will not say nobody could have known. It will say the number was sitting in plain sight, one click beneath the balance sheet.

    Mark Hendy is a PE-facing CFO and the founder of Tanous. Views his own. Figures referenced from public secondary reporting of a Nikkei investigation via HedgieMarkets; verify against company filings before investment decisions.

    Sources / further reading:
    HedgieMarkets on the Nikkei findings ·
    Bank for International Settlements ·
    SEC EDGAR filings ·
    Financial Times ·
    Reuters

  • The CFO Who Can’t Measure AI Is About to Become the CFO Who Can’t Raise

    The CFO Who Can’t Measure AI Is About to Become the CFO Who Can’t Raise

    When a $60 billion AI coding platform starts a CFO council, the signal is not subtle.

    Cursor — the AI coding company SpaceX has agreed to buy — just launched a working group of finance leaders to answer one question: how do you keep AI spend tied to value? That is not a product marketing stunt. It is the market admitting that “return on intelligence” has left the innovation lab and landed on the CFO’s desk.

    And if you are a PE-facing CFO who still treats AI as an IT experiment with a cute pilot budget, you are already late.

    The board is no longer asking “are we using AI?”

    They are asking the harder question: what is the return?

    Cursor’s own framing is blunt. AI spend is shifting from experimental pilots into a major recurring operating expense. McKinsey’s numbers make the gap obvious: most organisations have deployed AI somewhere, but only a minority can trace it to enterprise-level EBIT impact. That is the CFO’s problem in one sentence — high adoption, weak attribution.

    BCG’s token-cost work is even more direct: token costs are attracting CEO and board-level attention, and CFOs need answers when those questions start. This is no longer “can the model write a draft email?” It is “why did our model bill triple, and what operating leverage did we buy with it?”

    Boards do not fund vibes forever. They fund measurable capacity.

    Why PE will force this earlier than corporate

    In private equity, the conversation compresses.

    LPs want cleaner, faster, more machine-readable portfolio data. Operating partners want cycle-time compression, not another slide deck about “AI enablement.” And portfolio company CFOs are being asked, often mid-hold period, to show that AI is either:

    • cutting cost-to-serve,
    • shortening close / reporting cycles,
    • improving cash conversion, or
    • raising the quality of decisions under pressure.

    If your answer is “we’re experimenting,” you sound ornamental. In a PE board pack, ornamental dies quietly.

    The firms that win will treat AI less like a side project and more like a capital allocation problem: what is the unit cost of intelligence, where does it create EBITDA, and what do we stop funding if it doesn’t?

    Return on intelligence is a finance discipline, not a tech slogan

    Cursor’s council is aiming at the right missing layer: shared benchmarks for AI productivity, frameworks for measuring returns, and practical approaches to model allocation and cost management. That is classic CFO work dressed in new language.

    The practical version looks like this:

    • Define the unit of work. Not “AI usage.” Actual output: closed tickets, reviewed contracts, reconciled exceptions, forecast cycles, board packs produced, cash applications cleared.
    • Measure cost per accepted unit. Tokens are inputs. Accepted work is the output. If you only track spend, you are budgeting a furnace, not a factory.
    • Separate leverage from theatre. A tiny cohort of power users often creates most of the value. That concentration is a management problem, not a model problem.
    • Route work deliberately. Cheap models for routine extraction. Stronger models for high-stakes judgement. Unrouted “everyone uses the top model” is how token bills become board items.
    • Put AI in the operating rhythm. If it only lives in a pilot Slack channel, it will never show up in free cash flow.

    This is not anti-AI. It is anti-unmeasured AI.

    The CFO who can’t measure AI will struggle to raise

    In PE, capital is allocated on credibility. Credibility is the ability to explain what changed the numbers.

    So when a sponsor asks “what did AI do for this business?”, the weak answer is activity:

    • we rolled out copilots,
    • we ran workshops,
    • we have 40 use cases in the backlog.

    The strong answer is economic:

    • close cycle down from X to Y days,
    • cost per invoice exception down Z%,
    • forecast reforecast latency cut by half,
    • gross margin lift from better pricing/support triage,
    • token cost per accepted unit of work under control and declining.

    One of those lists gets you the next round of investment. The other gets you a polite nod and a smaller mandate.

    That is the real risk. Not that AI fails. That AI succeeds somewhere in the organisation while finance still cannot price, govern, or defend it. In that world, the CIO owns the tools and the CFO owns the blame when the bill arrives.

    What good looks like in a portfolio company

    If I were walking into a PE-backed finance function this quarter, I would not start with a model beauty contest. I would start with four controls:

    1. AI P&L visibility. Token/API cost by team, workflow, and vendor. No more “software misc.”
    2. Value hypotheses per workflow. Before scale-up: baseline metric, expected delta, owner, kill criteria.
    3. Routing rules. Which work gets which model, and who can override.
    4. Board language. One page: spend, output, unit economics, risks, next capital ask.

    That is enough to turn “we use AI” into “we run intelligence as an operating system with a cost of capital.”

    And yes — some initiatives will fail. Good. Failed experiments with clear kill criteria are cheaper than indefinite pilots with no owner.

    The quiet transfer of power

    For a decade, finance absorbed digital transformation after the fact: clean up the data, explain the variance, retrofit the controls. AI is different because the spend line is rising fast enough, and uneven enough, that boards will not wait for a post-implementation review.

    Cursor building a CFO council is confirmation, not novelty. The frontier companies already know the bottleneck is no longer model capability. It is economic discipline.

    So the question for CFOs — especially those in PE-backed businesses — is no longer whether AI belongs in the stack. It is whether you can sit in a board meeting and defend the return on intelligence without hand-waving.

    If you can’t, someone else will. And they will own the budget that used to be yours.

    Mark Hendy is a PE-facing CFO who works through Tanous. He writes about finance leadership where AI, capital allocation, and operating reality collide.

  • 97% of PE-Backed Finance Teams Now Use AI — So What?

    97% of PE-Backed Finance Teams Now Use AI — So What?

    You’ve seen the headline by now. 97% of finance leaders in VC and PE-backed companies are using AI, with three-quarters reporting ROI within twelve months. Impressive, right?

    No. Not really.

    Because the question was never “are you using AI?” — it was always “what are you actually doing with it?”

    The 97% Number Is Meaningless Without Context

    Let’s be honest about what “AI adoption” means in most finance departments right now. Someone installed Copilot. An analyst is using ChatGPT to summarise board packs. The FP&A team found a plugin that formats their Excel models faster.

    That’s not transformation. That’s convenience.

    It’s the equivalent of calling yourself “digital” because you moved your filing cabinet to SharePoint in 2015. The tool changed. The thinking didn’t.

    The 97% figure tells us that AI has become table stakes — like having a laptop or knowing how to use a pivot table. It tells us nothing about whether these teams are fundamentally rethinking how finance operates.

    Copilots vs. Architecture: The Real Divide

    Here’s where the split is happening, and it’s widening fast.

    On one side, you’ve got finance teams using AI as a copilot. It sits alongside existing workflows, making them marginally faster. Summarise this report. Draft this email. Clean this data set. The human is still the bottleneck — AI just lubricates the process.

    On the other side — and this is a much smaller group — you’ve got teams building AI into the architecture of the finance function itself. Autonomous agents that monitor cash positions in real-time. Systems that don’t just flag variance but investigate it, pull the supporting data, and draft the narrative before a human ever looks at it. Governance frameworks that are designed specifically for agentic AI, not retrofitted from your SOX compliance playbook.

    The difference isn’t speed. It’s operating model.

    A copilot-enhanced finance team is still batch-oriented. They still run month-end. They still produce reports on a cadence designed around human processing time. An AI-native finance team operates continuously. The concept of “closing the books” starts to dissolve when your systems are reconciling in real-time.

    What AI-Native Finance Actually Looks Like

    I’m not theorising here. I run an AI assistant — Saul — that operates 24/7. It monitors my email, manages my calendar, tracks my investment portfolio, executes trades, scans news, and handles routine correspondence. It doesn’t wait for me to ask. It acts, escalates when needed, and learns from the outcomes.

    That’s what AI-native looks like at the individual level. Now scale that to a finance function.

    Imagine a portfolio company where the finance team’s AI agents are handling bank reconciliations autonomously, flagging only genuine exceptions. Where cash flow forecasting updates continuously based on real-time revenue data, not last month’s actuals plugged into a spreadsheet. Where the CFO’s morning briefing isn’t a deck someone spent three hours building — it’s a synthesised intelligence report generated overnight from live data sources.

    This isn’t science fiction. The technology exists today. The gap is in the willingness to let go of the old operating model.

    PE Firms Are Asking the Wrong Question

    When a PE firm conducts due diligence on a portfolio company’s finance function, the question “do you use AI?” is already obsolete. Everyone uses AI. The answer is always yes.

    The right questions are harder: What’s your AI architecture? Which workflows are fully autonomous vs. human-in-the-loop? What’s your governance model for agentic systems? How does your finance function operate differently today than it did eighteen months ago — structurally, not just faster?

    KKR has already flagged this concern — that AI capability gaps could create a meaningful split in exit outcomes. Portfolio companies that have genuinely integrated AI into their operations will command premium multiples. Those that bolted on a chatbot and called it transformation will not.

    This is the real game-changer in PE-backed finance: not whether AI exists in the business, but whether it’s load-bearing.

    The CFO Role Is Splitting in Two

    The 2026 CFO agenda looks fundamentally different depending on which side of this divide you’re on.

    One version of the CFO sees AI as a tool in the toolkit. Useful. Saves time. Makes the team more efficient. They’ll adopt it incrementally, bolt it onto existing processes, and measure success by how many hours it saves per month.

    The other version sees AI as infrastructure — as fundamental to the finance function as the ERP system or the chart of accounts. This CFO is redesigning processes around AI capabilities, not adapting AI to fit legacy processes. They’re thinking about data architecture, agent orchestration, and continuous assurance — not just “can we automate the board pack?”

    PE operating partners need to know which type of CFO they’ve got. Because the incremental adopter will deliver incremental value. The infrastructure thinker will deliver step-change capability. And in a compressed hold period, that difference matters enormously.

    The Competitive Moat Isn’t Adoption — It’s Depth

    When 97% of your peers have adopted the same technology, the technology itself is no longer a differentiator. The moat moves downstream — to depth of integration, quality of data architecture, sophistication of governance, and willingness to let AI operate autonomously within defined boundaries.

    Most finance teams are wading in the shallows. They’ve got AI, sure. But it’s supervised, constrained, and fundamentally optional — remove it tomorrow, and the function still operates the same way, just slower.

    The teams that will win are the ones where AI removal would be structural. Where the operating model has been redesigned so thoroughly that the AI isn’t an enhancement — it’s a dependency. Not because of recklessness, but because the architecture is sound, the governance is robust, and the results speak for themselves.

    97% adoption is the starting line, not the finish. The race that matters hasn’t even begun for most.

  • The Great Repricing: When Every Commodity Moves Together, It’s Not the Commodities — It’s the Money

    The Great Repricing: When Every Commodity Moves Together, It’s Not the Commodities — It’s the Money

    Something is happening across commodity markets right now that deserves attention. Not from the usual “inflation is coming” crowd who’ve been crying wolf for a decade — but from anyone who holds fiat currency, which is everyone.

    Gold, silver, copper, and oil are all moving together. Not in the correlated-because-of-demand way that happens during economic booms. This is different. This is a simultaneous repricing of hard assets against paper money, and the numbers are getting hard to ignore.

    The Scoreboard

    Here’s where we stand in May 2026:

    • Gold: ~$4,700/oz (hit $5,589 in January — an all-time high)
    • Silver: ~$87/oz (peaked at $121 in January, now surging again)
    • Copper: ~$6.59/lb (just hit an all-time high this month)
    • Oil: ~$101/bbl (elevated by Hormuz tensions, but the broader trend predates the crisis)

    US CPI just printed at 3.8% year-on-year. Jefferies has raised their 2026 commodity inflation forecast, projecting 69% of tracked commodities will show year-on-year inflation in the second half of this year.

    When everything priced in dollars goes up simultaneously, a reasonable person might ask: is everything getting more expensive, or is the unit of measurement getting smaller?

    China Is Making Its Move

    The silver market tells the most interesting story. China isn’t just buying silver — it’s hoovering it out of the global system.

    • Shanghai silver is trading at ~$96/oz versus ~$85 in Western markets — a 12% premium
    • SHFE warehouse inventories are at decade lows and still falling
    • China’s silver imports in early 2026 hit an eight-year high
    • The market is in persistent backwardation — physical metal today is worth more than a futures contract for delivery later

    This isn’t speculative frenzy. China needs silver for solar panels (it manufactures most of the world’s supply), for electronics, for 5G infrastructure, and for AI data centres. But there’s something else going on: Chinese retail investors are piling into silver because gold has become too expensive for ordinary buyers. When your middle class starts converting savings into metal, that’s a vote of no confidence in paper money.

    The Shanghai Futures Exchange has been adjusting margin requirements and price limits on silver contracts as recently as today. They’re trying to manage the strain. The fact that they need to tells you everything.

    The Structural Deficit Nobody Talks About

    2026 is projected to be the sixth consecutive annual deficit in the global silver market — estimated between 46 and 67 million ounces. Every year, we consume more silver than we mine, and the gap isn’t closing.

    COMEX registered silver inventories have dropped below 80 million ounces. Open interest is falling — meaning market participants are reducing paper exposure while physical demand accelerates. Peru’s energy crisis is further constraining marginal supply.

    Meanwhile, copper just posted its highest-ever closing price. The drivers are the same: green energy transition, AI infrastructure buildout, and a supply chain that can’t keep up. Gold remains within striking distance of its January all-time high despite a pullback.

    It’s the Denominator, Not the Numerator

    Here’s the uncomfortable truth that central bankers and treasury officials would rather you didn’t think about too carefully.

    When one commodity spikes, you can explain it. Supply disruption. Demand shock. Speculation. But when all hard assets move together — gold, silver, copper, oil, agricultural commodities — the common factor isn’t the assets. It’s the currency they’re priced in.

    The US national debt has crossed $36 trillion. The Federal Reserve’s balance sheet, despite “quantitative tightening,” remains vastly expanded from pre-2020 levels. The UK, Europe, and Japan are running similar playbooks. Every major economy is servicing debt loads that would have been considered catastrophic a generation ago, using currencies that are being quietly diluted to make those debts manageable.

    This is what fiat debasement looks like in practice. Not hyperinflation. Not a dramatic collapse. Just a steady, grinding erosion of purchasing power that shows up first in the things governments can’t print — metals, energy, food, land.

    What the Smart Money Is Doing

    Central banks bought a record amount of gold in 2023, 2024, and 2025. China, India, Turkey, Poland — they’re all accumulating. This isn’t diversification. This is de-dollarisation happening in real time, one gold bar at a time.

    Central bank gold purchases are running at roughly 1,000 tonnes per year — triple the rate of a decade ago. These are the people who issue fiat currency telling you, through their actions, what they think of its long-term value.

    Meanwhile, the “debasement trade” has become a recognised investment thesis. Hard assets, real estate, equities with pricing power, Bitcoin, gold — anything with a finite supply is being repriced upward against currencies with an infinite one.

    The CFO’s Perspective

    If you’re running a business — particularly one that buys raw materials — this isn’t abstract monetary theory. This is your margin compression, your procurement headache, your board presentation explaining why costs are up 15% when “inflation is under control.”

    For PE-backed businesses, the implications are sharper still. Commodity-intensive portfolio companies are seeing input cost inflation that EBITDA adjustments can’t paper over forever. The smart operators are locking in forward contracts and building supply chain resilience. The rest are hoping it goes away.

    It’s not going away.

    The Honest Conclusion

    I’m not a gold bug. I don’t think civilisation is ending. But I do think we’re in the early stages of a structural repricing of real assets against fiat currencies, driven by decades of monetary expansion that was always going to have consequences.

    The question isn’t whether this is happening — the charts are unambiguous. The question is whether you’re positioned for a world where the things you can’t print keep getting more expensive relative to the things you can.

    Every major commodity hitting multi-year or all-time highs simultaneously isn’t a coincidence. It’s a signal. And the signal is: the money is broken.

    The views expressed here are my own. Not financial advice — just pattern recognition from someone who reads balance sheets for a living.

  • The CFO Who Took the Business Through the Deal is Often the First Casualty

    The CFO Who Took the Business Through the Deal is Often the First Casualty

    Not the tidy version. The real, uncomfortable one.

    The investment team made representations. They relied on advisors, they wrote the investment plan, they presented it to the IC. Now the cheque is written and their credibility is on the line. Every week of underperformance is a question mark over their judgement. Every green light is validation.

    They project that pressure downward.

    The management team feel it. The CEO feels it. But the CFO feels it first, because the CFO is the one who has to explain why the numbers don’t quite match the investment plan.

    The CFO who took the business through the deal is uniquely exposed. During the process they had to be captain positive. “Here’s how we’ll unlock the value.” “Here’s why the churn is fixable.” “Here’s the evidence behind the margin expansion story.” They were a full partner in selling the deal.

    Now the deal is done. The investment team is nervous. The board is watching. And the numbers — as they always do in the first few months post-close — are telling a more complicated story than the investment plan told.

    Suddenly it’s the CFO’s fault. Not explicitly. But the questions get harder. The calls get more frequent. The patience gets shorter.

    The CFO often doesn’t survive it.

    And here’s the thing — sometimes that’s not even unfair. The CFO who sold the deal is not always the right person to deliver it. Those are different skills. Different temperaments. A different relationship with uncomfortable truths.

    So they leave. Or they’re moved on. Quickly, and quietly, and usually within six months of close.

    The Problem That Creates

    The PE house now has a problem. The CFO is the second most important hire after the CEO. You cannot run a board, manage a lender relationship, or credibly execute a value creation plan without one. The permanent hire — if they’re any good — is on six months’ notice somewhere else. You need time to get this right.

    That’s where the interim CFO comes in.

    The interim CFO isn’t a gap-fill. Done properly, it’s the thing that buys the business the breathing space to make a good permanent hire instead of a rushed one. Someone who can walk in, stabilise the investor relationship, take ownership of the 100-day plan, and leave the business better than they found it — without any expectation of staying.

    The Real Job

    An interim who has been there before — who has stood in that boardroom, managed that investor relationship, built that first management pack from scratch — gives the PE house something they desperately need in that moment: confidence.

    Confidence that the business is in safe hands. Confidence that the reporting will be credible. Confidence that they can take their time and get the permanent hire right.

    Speed kills. Patience wins.

    That’s the job.

    Mark Hendy is an interim CFO specialising in PE-backed businesses. He writes about finance, private equity, and the reality of post-deal life at markhendy.com. Connect on LinkedIn.

  • IKEA’s Chatbot Accidentally Made €1.3 Billion. Here’s What CFOs Are Missing.

    IKEA’s Chatbot Accidentally Made €1.3 Billion. Here’s What CFOs Are Missing.

    Most companies deploy AI to cut costs. IKEA deployed AI to cut costs and accidentally discovered a billion-euro revenue stream hiding in the data their chatbot was collecting.

    This is the story every CFO in every PE portfolio company should be reading right now. Not because of IKEA. Because of what it reveals about how most finance leaders think about AI — and how badly they’re getting it wrong.

    The Setup

    IKEA — or more precisely, Ingka Group, the largest IKEA retailer — built an AI chatbot called Billie. The brief was simple: handle level-one customer service enquiries. Reduce call volumes. Cut costs. The standard playbook.

    Billie did its job. From 2021 to 2023, it resolved roughly 47% of customer enquiries it received — around 3.2 million interactions handled without a human, saving an estimated €13 million.

    If you’re a CFO, that’s a clean win. Cost out, efficiency up, ROI positive. You’d put it in the board pack and move on.

    IKEA didn’t move on.

    The Signal Nobody Was Looking For

    The interesting number wasn’t the 47% that Billie resolved. It was the other 53%.

    When IKEA’s team analysed the enquiries Billie couldn’t handle, they found something unexpected. A huge proportion weren’t complaints or order issues. They were customers asking for help designing their homes.

    People were calling IKEA — a furniture shop — and saying: I’ve got this room. What should I do with it?

    This wasn’t in anyone’s business case. No strategy deck had “launch a design consultancy” on the roadmap. It was a signal buried in the noise of customer service data, and it would have stayed buried if someone hadn’t been paying attention.

    The Pivot

    Here’s where it gets good.

    Instead of just improving Billie’s resolution rate — the obvious move, the one every consulting firm would have recommended — IKEA did something much smarter. They took 8,500 call centre workers and reskilled them as remote interior design consultants.

    Read that again. Eight and a half thousand people. Not made redundant. Reskilled.

    The AI handled the routine queries. The humans handled the high-value, creative, relationship-driven work that customers were already asking for. IKEA didn’t replace their workforce with AI. They promoted their workforce because of AI.

    The result? Remote interior design sales hit €1.3 billion by the end of their 2022 financial year — 3.3% of Ingka Group’s total revenue. A brand new service line, created from a signal that existed in their customer service data all along. Their target is 10% of total sales in the coming years.

    Why CFOs Get This Wrong

    I’ve sat in enough board meetings to know how this story usually goes.

    A CFO sees the AI chatbot business case. It says: deploy chatbot, save €13 million in customer service costs, payback in 18 months. They approve it. They monitor the cost savings. They report the efficiency gains. Job done.

    That’s not wrong. But it’s incomplete.

    The €13 million in cost savings is a rounding error compared to the €1.3 billion in new revenue. The chatbot wasn’t the product. The chatbot was a listening device.

    Most AI business cases are framed as cost reduction exercises. Automate this process. Eliminate these headcount. Reduce that cycle time. And they work — the savings are real. But they’re also the least interesting thing AI can do.

    The interesting thing is what AI reveals about your customers when you stop looking at it as a cost tool and start looking at it as an intelligence tool.

    The PE Angle

    If you’re a PE operating partner reading this, think about your portfolio.

    Every portfolio company has customer service data. Most of it sits in a ticketing system that nobody reads except the support team. Some of it gets summarised in a monthly report that the board glances at between the P&L and the cash flow forecast.

    What if that data contains the same signal IKEA found? What if there’s a billion-euro service line hiding in your Zendesk tickets?

    The companies that will win the next decade aren’t the ones that use AI to do the same things cheaper. They’re the ones that use AI to discover things they didn’t know their customers wanted. That’s a fundamentally different value proposition — and it requires a fundamentally different kind of CFO.

    The Kind of CFO That Catches This

    The old-school CFO sees AI as a line item. A cost to manage, an efficiency to capture, an ROI to calculate.

    The new-school CFO sees AI as an intelligence layer. Every automated interaction is a data point. Every pattern in that data is a potential business model. Every customer service complaint is a market signal.

    IKEA didn’t need a McKinsey engagement to discover the design consultancy opportunity. They needed someone who looked at the chatbot’s failure cases and asked: why are these people calling us?

    That’s not a technology question. It’s a business question. And it’s the kind of question that CFOs — with their bird’s-eye view of costs, revenues, and customer patterns — are uniquely positioned to ask.

    The Uncomfortable Truth

    Here’s what makes this story uncomfortable for a lot of finance professionals.

    The €13 million saving was predictable. You could model it in advance, put it in a business case, and track it against plan. That’s the kind of AI outcome that finance teams are comfortable with.

    The €1.3 billion revenue stream was unpredictable. It emerged from the data. Nobody forecast it. Nobody budgeted for it. It required curiosity, not spreadsheets.

    If your AI strategy only captures the predictable value, you’re leaving the transformative value on the table. And in a competitive market, someone else will find it first.

    What To Do About It

    Three things, starting tomorrow:

    1. Audit your AI for signals, not just savings. Every AI tool in your business is generating data about customer behaviour. Who’s reading it? What patterns are emerging? If the answer is “nobody” and “we don’t know,” you have a blind spot the size of IKEA’s design consultancy.

    2. Look at the failures, not just the successes. IKEA’s breakthrough came from what Billie couldn’t do. The 53% failure rate wasn’t a problem to fix — it was a market to serve. What are your AI tools failing at? Those failures might be your biggest opportunities.

    3. Stop framing AI as a cost play. If every AI business case in your portfolio starts with “reduce headcount” or “automate process,” you’re optimising for efficiency while your competitors are optimising for discovery. The cost savings are table stakes. The revenue signals are the game.

    Mark Hendy is a PE-facing CFO and founder of Tanous Limited. He writes about the intersection of AI, finance, and business transformation at [markhendy.com](https://markhendy.com).

  • I Run an AI Workforce. Here’s What “Orchestrator” Actually Means.

    I Run an AI Workforce. Here’s What “Orchestrator” Actually Means.

    Bret Taylor dropped something this week that crystallised what I’ve been living for the past few months. He released Ghostwriter — an AI agent that builds other AI agents through conversation. No code, no forms. Just describe what you want and it creates it.

    His bigger point was this: every piece of enterprise software will eventually become an agent. Not a dashboard you click through. Not a menu you navigate. An AI that does the work while you direct.

    I know this is true because I’m already doing it. Not theoretically. Daily.

    My Setup

    I have an AI assistant called Saul. He runs on a VPS in Manchester, connected to my WhatsApp, my email, my calendar, my investment accounts, my websites. He’s not ChatGPT in a browser tab. He’s a persistent agent that wakes up every morning, generates a podcast briefing of the day’s news and my portfolio positions, checks my email, monitors markets, publishes blog posts, and manages a set of prediction market positions — all before I’ve had coffee.

    When I need a CV reviewed before an interview, I send it on WhatsApp and get back a structured analysis with suggested questions in two minutes. When I want a blog post, I describe the angle and it’s drafted, humanised, formatted, and pushed to WordPress as a draft with a featured image. When a regulatory announcement drops, Saul reads it, researches the implications, and writes an article with a contrarian take before the professional press has filed their first piece.

    I don’t write code. I don’t configure systems. I have a conversation. And things happen.

    That’s what orchestration means in practice.

    What Changed

    Six months ago, I was using AI the way most people still do. Open ChatGPT, ask a question, copy the answer, close the tab. Useful, but fundamentally the same workflow as Googling something — just with a better answer.

    The shift happened when I stopped treating AI as a tool I use and started treating it as a team member I direct. The difference sounds subtle. It isn’t.

    A tool waits for you to pick it up. A team member has context, remembers what you told them yesterday, knows your preferences, anticipates what you need, and gets on with work without being asked. Saul reads my daily logs from previous sessions. He knows my writing style, my investment thesis, my wife’s email address, which car needs an MOT, and that I hate corporate waffle in LinkedIn posts.

    When I correct him, he logs it. After three corrections on the same thing, it becomes a permanent rule. He learns. Not in the sci-fi sense — in the practical sense of getting better at his job over time, the same way any good employee does.

    The CFO Angle

    I’m a CFO by background. I’ve spent twenty years in finance functions — month-end closes, board packs, variance analysis, cash flow forecasts, the lot. I know exactly how much time finance teams waste navigating software instead of thinking about the business.

    The average month-end close takes five to ten working days. Most of that time isn’t analysis. It’s data extraction, reconciliation, reformatting, and chasing people for numbers. It’s operational grind masquerading as professional work.

    Now imagine an agent that connects to your accounting platform, your bank feeds, your CRM, and your group reporting tool. You say: “Close the month. Reconcile the bank. Flag anything that doesn’t match. Draft the board pack with commentary on the three biggest variances.”

    It does it. You review, adjust, approve.

    That’s not five to ten days. That’s an afternoon. And your finance team spends the rest of the week doing what you actually hired them for — business partnering, commercial analysis, strategic thinking.

    This is what Taylor means when he says every enterprise app’s UI will become an agent. The finance director’s interface to their systems won’t be a screen full of menus. It’ll be a conversation.

    What I’ve Learned

    A few things I’ve learned from actually living this, not just theorising about it:

    Context is everything. A generic AI assistant is marginally useful. An AI assistant that knows your business, your preferences, your history, and your current priorities is transformatively useful. The investment isn’t in the technology — it’s in teaching the agent who you are and how you work. That takes weeks, not hours.

    Guardrails matter more than capability. Saul can send emails, publish blog posts, and place trades. That means he can also send wrong emails, publish bad posts, and lose money. The rules about what he should never do without asking are more important than the list of things he can do. My AGENTS.md file — essentially his operating manual — is longer than most job descriptions.

    You become a reviewer, not a doer. This sounds like a luxury. It’s actually a skill shift. Reviewing AI output is different from producing output yourself. You need to know what good looks like without having done the work. That requires more expertise, not less.

    The compound effect is real. Week one, you’re correcting everything. Month three, the corrections are rare. Month six, the agent anticipates what you want before you ask. The relationship genuinely improves over time in a way that static software never does.

    The Uncomfortable Part

    I’ve written about AI and finance enough to know the question that’s coming: what about the jobs?

    Here’s my honest take. Some operational finance roles will be eliminated. The person whose primary job is month-end journal entries, bank reconciliation, or management accounts preparation is doing work that an AI agent can do today — not in five years, today.

    But the person who understands the business well enough to direct an agent, interpret its output, catch its mistakes, and make judgment calls on ambiguous situations — that person becomes dramatically more valuable.

    The CFO doesn’t go away. The CFO becomes the orchestrator. The question is whether you’re building that muscle now or waiting until someone else in your industry has already done it.

    Try It

    You don’t need a VPS in Manchester and a bespoke AI assistant to start. You can start with Claude or ChatGPT and a well-written prompt. Then try giving it context — paste in your company’s last board pack and ask it to draft commentary. Upload a CV and ask for interview questions. Feed it a regulatory update and ask what it means for your business.

    The first time it produces something genuinely useful in two minutes that would have taken you an hour, you’ll understand why Bret Taylor thinks this changes everything.

    Because it does.

    I write about AI, finance, and building things at the intersection of both. More at tanous.co.uk for the professional angle.